saving

tests/unit/test_race_plan.py

@@ -4,8 +4,9 @@ from __future__ import annotations
 
 import numpy as np
 import pandas as pd
+import pytest
 
-from openrun.model import calibrate_tl_per_km, personal_records
+from openrun.model import calibrate_tl_per_km, personal_records, plan_to_daily_load
 
 
 def _act(aid: int, dist_km: float, pace: float, when: str = "2026-01-01") -> dict:
@@ -114,3 +115,62 @@ def test_calibrate_tl_per_km_returns_nan_when_empty(tmp_conn) -> None:
     result = calibrate_tl_per_km(tmp_conn, lookback_days=None)
     assert result["n"] == 0
     assert np.isnan(result["median"])
+
+
+# ---------------------------------------------------------------------------
+# plan_to_daily_load
+# ---------------------------------------------------------------------------
+
+def _plan_row(week_start: str, weekly_km: float, long_run_km: float) -> dict:
+    return {"week_start": pd.Timestamp(week_start), "weekly_km": weekly_km, "long_run_km": long_run_km}
+
+
+def test_plan_distributes_weekly_km_across_7_days() -> None:
+    """A single 70 km / 28 km long-run week should emit 7 daily entries summing to 70·tl_per_km."""
+    plan = pd.DataFrame([_plan_row("2026-05-04", 70.0, 28.0)])  # Mon 2026-05-04
+    series = plan_to_daily_load(plan, tl_per_km=10.0)
+    assert len(series) == 7
+    assert series.sum() == pytest.approx(700.0)
+    # Saturday (default 40 %) gets the biggest single load.
+    saturday = pd.Timestamp("2026-05-09")
+    assert series.idxmax() == saturday
+    assert series[saturday] == pytest.approx(700.0 * 0.40)
+
+
+def test_plan_race_week_uses_race_day_tl_per_km() -> None:
+    """If a `race_dates` entry is inside the week, race day gets long_run_km × race_tl,
+    and the remaining km distribute across Mon/Wed/Fri at training rate."""
+    plan = pd.DataFrame([_plan_row("2026-05-04", 30.0, 20.0)])
+    series = plan_to_daily_load(
+        plan,
+        tl_per_km=11.0,
+        race_day_tl_per_km=7.0,
+        race_dates=("2026-05-09",),  # Saturday
+    )
+    sat = pd.Timestamp("2026-05-09")
+    assert series[sat] == pytest.approx(20.0 * 7.0)  # race load
+    # Remaining km = 30 - 20 = 10. Spread across Mon/Wed/Fri @ 11.0 → 110 / 3 each.
+    for d in [pd.Timestamp("2026-05-04"), pd.Timestamp("2026-05-06"), pd.Timestamp("2026-05-08")]:
+        assert series[d] == pytest.approx(110.0 / 3, rel=1e-6)
+
+
+def test_plan_empty_returns_empty_series() -> None:
+    assert plan_to_daily_load(pd.DataFrame(columns=["week_start", "weekly_km", "long_run_km"]),
+                              tl_per_km=10.0).empty
+
+
+def test_plan_zero_weekly_km_yields_zeros() -> None:
+    """A rest week (weekly_km=0) shouldn't crash and should produce 7 zero-load days."""
+    plan = pd.DataFrame([_plan_row("2026-05-04", 0.0, 0.0)])
+    series = plan_to_daily_load(plan, tl_per_km=10.0)
+    assert (series == 0).all()
+    assert len(series) == 7
+
+
+def test_plan_custom_weekday_weights() -> None:
+    """Pass a flat 1/7 split, expect equal load each day."""
+    plan = pd.DataFrame([_plan_row("2026-05-04", 70.0, 28.0)])
+    weights = {i: 1 / 7 for i in range(7)}
+    series = plan_to_daily_load(plan, tl_per_km=10.0, weekday_weights=weights)
+    assert series.std() == pytest.approx(0.0, abs=1e-9)
+    assert series.iloc[0] == pytest.approx(100.0)